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Record W1981123751 · doi:10.1080/19397030903348710

Flax fibre quality and influence on interfacial properties of composites

2009· article· en· W1981123751 on OpenAlexaff
Jonn A. Foulk, Michael Fuqua, Chad A. Ulven, M. Alcock

Bibliographic record

VenueInternational Journal of Sustainable Engineering · 2009
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsOrthopaedic Innovation Centre
Fundersnot available
KeywordsComposite numberComposite materialMaterials scienceFinenessKenafWaxGlass fiberFiber

Abstract

fetched live from OpenAlex

Flax fibre holds the potential to serve as an alternative to glass fibre as reinforcement in composite applications. To fully achieve this, the interaction between fibre and matrix must be improved and more consistently controlled. Only then will industry accept natural fibres as a sustainable engineering material choice. Traditionally, interfacial strength improvement has been accomplished through expensive and time consuming chemical surface modification(s). To achieve improved market potential and viability, new methods of developing composite ready flax fibre must be researched and developed through an assessment of the impact of fibre traits for unmodified fibre. Metal, fungal, bacterial, wax and glucose content were examined in this study to determine their correlative effects upon interfacial adhesion, as were fibre characteristics such as colour, density, fineness, fibreshape thickness, conductivity and pH levels. Composite performance was evaluated using fibre pullout and interfacial shear strength tests. These first attempts at correlating as-received flax fibre traits and resulting flax fibre composite properties contain the initial steps towards identifying key flax fibre characteristics that influence composite performance so that proper growth and fibre processing approaches can be developed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.252
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2009
Admission routes1
Has abstractyes

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